Abstract
We present TabEE, Tabular Embedding Explanations, a framework designed to generate explanations for interpreting tabular embedding models. Our framework aims to furnish both local and global explanations for the original data, facilitating the detection of potential flaws in embedding models. TabEE is versatile and compatible with any tabular embedding algorithm, as it adheres to the black box perspective of embedding models. The generated explanations also enable comparisons between multiple embedding models. This demonstration illustrates the effectiveness of TabEE in providing interpretable insights into tabular embedding models, contributing to improved model understanding and credibility assessment.
Original language | English |
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Pages (from-to) | 4285-4288 |
Number of pages | 4 |
Journal | Proceedings of the VLDB Endowment |
Volume | 17 |
Issue number | 12 |
DOIs | |
State | Published - 2024 |
Event | 50th International Conference on Very Large Data Bases, VLDB 2024 - Guangzhou, China Duration: 24 Aug 2024 → 29 Aug 2024 |
All Science Journal Classification (ASJC) codes
- Computer Science (miscellaneous)
- General Computer Science